{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Synthea_Medications_Tensorflow.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPz/q9PpZiKd7HlMz+utUv1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"yXL-yD93yOkG"},"source":["# Example - Simple training on Synthea Medications dataset through Tensorflow\n","    \n","Based on [Tensorflow - Load_CSV_Data Tutorial](https://www.tensorflow.org/tutorials/load_data/csv)\n","\n","Authors:\n","- Pavlos Papadopoulos · GitHub:  [@pavlos-p](https://github.com/pavlos-p)\n"]},{"cell_type":"markdown","metadata":{"id":"CtPBd8bBkGyy"},"source":["# **Setup**"]},{"cell_type":"code","metadata":{"id":"YFq_49Vkj1jd","executionInfo":{"status":"ok","timestamp":1607000735921,"user_tz":0,"elapsed":1834,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["import pandas as pd\n","import numpy as np\n","\n","# Make numpy values easier to read.\n","np.set_printoptions(precision=3, suppress=True)\n","\n","import tensorflow as tf\n","from tensorflow.keras import layers\n","from tensorflow.keras.layers.experimental import preprocessing"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8CN8mkUYkQRw"},"source":["# **Mixed data types**\n","Our Synthea generated \"medications.csv\" contains data related to the medications of the patients. The task on this dataset is to predict the medication that the patient should follow."]},{"cell_type":"markdown","metadata":{"id":"tPgUGT6TlQmz"},"source":["## **Option 1:** \n","Use Google Drive space to load the \".CSV\"."]},{"cell_type":"markdown","metadata":{"id":"l0VpcLxmszpO"},"source":["If you are using Google Colab, change the `Runtime > Change runtime type > Hardware accelerator > GPU`, so the training later on will be faster. "]},{"cell_type":"markdown","metadata":{"id":"IdbPRA2mmgOD"},"source":["Link your Google Drive account to the Google Colab notebook."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hYZCCpgvkL-9","executionInfo":{"status":"ok","timestamp":1607000755795,"user_tz":0,"elapsed":17135,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"a27a6954-f971-471b-82ee-e95f2b893ba3"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"P-cpJRp4mls3"},"source":["Find the correct directory using common bash commands starting with exclamation mark (e.g. `!ls`, `!pwd`)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZX8hn3SFls7C","executionInfo":{"status":"ok","timestamp":1607000758284,"user_tz":0,"elapsed":507,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"c3947de2-205c-4d34-a0e0-281b039590a0"},"source":["cd drive/MyDrive/Colab\\ Notebooks"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/Colab Notebooks\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"1UjS_wftm7pc"},"source":["Check if the `medications.csv` exists."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RsJ7PwYql2x2","executionInfo":{"status":"ok","timestamp":1607000760490,"user_tz":0,"elapsed":441,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"1835ff3a-3ad6-4623-917c-939136186c56"},"source":["import os.path\n","os.path.isfile(\"medications.csv\")"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":374},"id":"QtqYlOx5nzEQ","executionInfo":{"status":"ok","timestamp":1607000763867,"user_tz":0,"elapsed":1901,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"3a7ca849-124d-4e91-cc83-7a1f06349455"},"source":["synthea = pd.read_csv(\"medications.csv\")\n","synthea.head()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>START</th>\n","      <th>STOP</th>\n","      <th>PATIENT</th>\n","      <th>PAYER</th>\n","      <th>ENCOUNTER</th>\n","      <th>CODE</th>\n","      <th>DESCRIPTION</th>\n","      <th>BASE_COST</th>\n","      <th>PAYER_COVERAGE</th>\n","      <th>DISPENSES</th>\n","      <th>TOTALCOST</th>\n","      <th>REASONCODE</th>\n","      <th>REASONDESCRIPTION</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2012-03-01T06:14:03Z</td>\n","      <td>2012-03-22T06:14:03Z</td>\n","      <td>7d3e489a-7789-9cd6-2a1b-711074af481b</td>\n","      <td>d47b3510-2895-3b70-9897-342d681c769d</td>\n","      <td>d989eaad-4105-1f1b-62f3-bb15e024ec2c</td>\n","      <td>562251</td>\n","      <td>Amoxicillin 250 MG / Clavulanate 125 MG Oral T...</td>\n","      <td>24.26</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>24.26</td>\n","      <td>444814009.0</td>\n","      <td>Viral sinusitis (disorder)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2013-05-08T00:17:55Z</td>\n","      <td>2013-05-19T00:17:55Z</td>\n","      <td>3829c803-1f4c-74ed-0d8f-36e502cadd0f</td>\n","      <td>6e2f1a2d-27bd-3701-8d08-dae202c58632</td>\n","      <td>a4f8f0e3-a654-96ec-f154-a95119683058</td>\n","      <td>834061</td>\n","      <td>Penicillin V Potassium 250 MG Oral Tablet</td>\n","      <td>32.88</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>32.88</td>\n","      <td>43878008.0</td>\n","      <td>Streptococcal sore throat (disorder)</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2013-11-02T00:43:55Z</td>\n","      <td>2014-01-01T00:43:55Z</td>\n","      <td>3829c803-1f4c-74ed-0d8f-36e502cadd0f</td>\n","      <td>6e2f1a2d-27bd-3701-8d08-dae202c58632</td>\n","      <td>ef0554c1-eca1-9f75-b5d3-44b432766db9</td>\n","      <td>313820</td>\n","      <td>Acetaminophen 160 MG Chewable Tablet</td>\n","      <td>6.18</td>\n","      <td>0.0</td>\n","      <td>2</td>\n","      <td>12.36</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2018-05-22T06:14:03Z</td>\n","      <td>2018-05-22T06:14:03Z</td>\n","      <td>7d3e489a-7789-9cd6-2a1b-711074af481b</td>\n","      <td>d47b3510-2895-3b70-9897-342d681c769d</td>\n","      <td>87cae904-0417-df2f-99ac-65baf83cc201</td>\n","      <td>896209</td>\n","      <td>60 ACTUAT Fluticasone propionate 0.25 MG/ACTUA...</td>\n","      <td>25.67</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>25.67</td>\n","      <td>185086009.0</td>\n","      <td>Chronic obstructive bronchitis (disorder)</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2018-05-22T06:14:03Z</td>\n","      <td>2019-05-28T06:14:03Z</td>\n","      <td>7d3e489a-7789-9cd6-2a1b-711074af481b</td>\n","      <td>d47b3510-2895-3b70-9897-342d681c769d</td>\n","      <td>87cae904-0417-df2f-99ac-65baf83cc201</td>\n","      <td>896209</td>\n","      <td>60 ACTUAT Fluticasone propionate 0.25 MG/ACTUA...</td>\n","      <td>34.82</td>\n","      <td>0.0</td>\n","      <td>12</td>\n","      <td>417.84</td>\n","      <td>185086009.0</td>\n","      <td>Chronic obstructive bronchitis (disorder)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  START  ...                          REASONDESCRIPTION\n","0  2012-03-01T06:14:03Z  ...                 Viral sinusitis (disorder)\n","1  2013-05-08T00:17:55Z  ...       Streptococcal sore throat (disorder)\n","2  2013-11-02T00:43:55Z  ...                                        NaN\n","3  2018-05-22T06:14:03Z  ...  Chronic obstructive bronchitis (disorder)\n","4  2018-05-22T06:14:03Z  ...  Chronic obstructive bronchitis (disorder)\n","\n","[5 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"eZzt0kpbnBz4"},"source":["## **Option 2:** \n","Use Local space to load the \".CSV\"."]},{"cell_type":"markdown","metadata":{"id":"LlkG5oIBnKBY"},"source":["In the PyVertical repo, the `medications.csv` file is at `PyVertical/data/medication.csv`. If you are currently at `PyVertical/examples`, you can follow the example below without any modification to the code."]},{"cell_type":"code","metadata":{"id":"IzjsTWj7l6zP"},"source":["synthea = pd.read_csv(\"../data/medications.csv\")\n","synthea.head()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BxbdQ1E8n94i"},"source":["# **Continue**\n","After loading the file successfully and Displaying the First 5 Records of the file."]},{"cell_type":"markdown","metadata":{"id":"IBcuF7HTopQO"},"source":["Display the values of the dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NYhp1ZYBoll7","executionInfo":{"status":"ok","timestamp":1607000767842,"user_tz":0,"elapsed":473,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"c93f67f0-87f9-4070-d18d-ccd9d9032444"},"source":["synthea.info()"],"execution_count":6,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 250620 entries, 0 to 250619\n","Data columns (total 13 columns):\n"," #   Column             Non-Null Count   Dtype  \n","---  ------             --------------   -----  \n"," 0   START              250620 non-null  object \n"," 1   STOP               236572 non-null  object \n"," 2   PATIENT            250620 non-null  object \n"," 3   PAYER              250620 non-null  object \n"," 4   ENCOUNTER          250620 non-null  object \n"," 5   CODE               250620 non-null  int64  \n"," 6   DESCRIPTION        250620 non-null  object \n"," 7   BASE_COST          250620 non-null  float64\n"," 8   PAYER_COVERAGE     250620 non-null  float64\n"," 9   DISPENSES          250620 non-null  int64  \n"," 10  TOTALCOST          250620 non-null  float64\n"," 11  REASONCODE         202454 non-null  float64\n"," 12  REASONDESCRIPTION  202454 non-null  object \n","dtypes: float64(4), int64(2), object(7)\n","memory usage: 24.9+ MB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HHLGMzXNpIgJ"},"source":["You can see that there are some missing values, some columns have \"250,620\" values, where some others have \"202,454\"."]},{"cell_type":"markdown","metadata":{"id":"Ln-TmDGzpf-f"},"source":["For this proof-of-concept, we remove the rows with empty values."]},{"cell_type":"code","metadata":{"id":"zjidAkpNolxb","executionInfo":{"status":"ok","timestamp":1607000771577,"user_tz":0,"elapsed":422,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["synthea.dropna(\n","    axis=0,\n","    how='any',\n","    thresh=None,\n","    subset=None,\n","    inplace=True\n",")"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IzWdyMrZptFf"},"source":["Display again the values of the dataset, to verify that the empty rows have been removed successfully."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2-bmt_ofpzx2","executionInfo":{"status":"ok","timestamp":1607000773678,"user_tz":0,"elapsed":459,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"95dba364-ef0d-4782-f06d-e5fe08d3d46b"},"source":["synthea.info()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 195032 entries, 0 to 250616\n","Data columns (total 13 columns):\n"," #   Column             Non-Null Count   Dtype  \n","---  ------             --------------   -----  \n"," 0   START              195032 non-null  object \n"," 1   STOP               195032 non-null  object \n"," 2   PATIENT            195032 non-null  object \n"," 3   PAYER              195032 non-null  object \n"," 4   ENCOUNTER          195032 non-null  object \n"," 5   CODE               195032 non-null  int64  \n"," 6   DESCRIPTION        195032 non-null  object \n"," 7   BASE_COST          195032 non-null  float64\n"," 8   PAYER_COVERAGE     195032 non-null  float64\n"," 9   DISPENSES          195032 non-null  int64  \n"," 10  TOTALCOST          195032 non-null  float64\n"," 11  REASONCODE         195032 non-null  float64\n"," 12  REASONDESCRIPTION  195032 non-null  object \n","dtypes: float64(4), int64(2), object(7)\n","memory usage: 20.8+ MB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0XwRPgENqBaz"},"source":["We can continue with the the data loading into Tensorflow. Additionally, we chose the field that we want to use for the prediction. In our case this is the \"`CODE`\" field."]},{"cell_type":"code","metadata":{"id":"hBN_Wcmsp3y-","executionInfo":{"status":"ok","timestamp":1607000777145,"user_tz":0,"elapsed":427,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["synthea_features = synthea.copy()\n","synthea_labels = synthea_features.pop('CODE')"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3wgH66zPqk3Q"},"source":["Because of the different data types and ranges you can't simply stack the features into  NumPy array and pass it to a `keras.Sequential` model. Each column needs to be handled individually. \n","\n","As one option, you could preprocess your data offline (using any tool you like) to convert categorical columns to numeric columns, then pass the processed output to your TensorFlow model. The disadvantage to that approach is that if you save and export your model the preprocessing is not saved with it. The `experimental.preprocessing` layers avoid this problem because they're part of the model."]},{"cell_type":"markdown","metadata":{"id":"minQReRiqn86"},"source":["In this example, you'll build a model that implements the preprocessing logic using [Keras functional API](https://www.tensorflow.org/guide/keras/functional.ipynb). You could also do it by [subclassing](https://www.tensorflow.org/guide/keras/custom_layers_and_models).\n","\n","The functional API operates on \"symbolic\" tensors. Normal \"eager\" tensors have a value. In contrast these \"symbolic\" tensors do not. Instead they keep track of which operations are run on them, and build representation of the calculation, that you can run later. Here's a quick example:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QcoYL8vmqfdz","executionInfo":{"status":"ok","timestamp":1607000785328,"user_tz":0,"elapsed":5792,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"16296cf7-b8f6-4661-a9eb-32229743e881"},"source":["# Create a symbolic input\n","input = tf.keras.Input(shape=(), dtype=tf.float32)\n","\n","# Do a calculation using is\n","result = 2*input + 1\n","\n","# the result doesn't have a value\n","result"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor 'AddV2:0' shape=(None,) dtype=float32>"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"kUh7JXrgqflr","executionInfo":{"status":"ok","timestamp":1607000786699,"user_tz":0,"elapsed":431,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["calc = tf.keras.Model(inputs=input, outputs=result)"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kGkWxLM_qxUB","executionInfo":{"status":"ok","timestamp":1607000788365,"user_tz":0,"elapsed":473,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"451e69a9-9f7b-42b1-a844-279732edf46f"},"source":["print(calc(1).numpy())\n","print(calc(2).numpy())"],"execution_count":12,"outputs":[{"output_type":"stream","text":["3.0\n","5.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"712mZaugq08K"},"source":["To build the preprocessing model, start by building a set of symbolic `keras.Input` objects, matching the names and data-types of the CSV columns."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FTDYsKY3qyzR","executionInfo":{"status":"ok","timestamp":1607000790294,"user_tz":0,"elapsed":496,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"8a2387f5-3690-40a1-b982-2fd5e279be75"},"source":["inputs = {}\n","\n","for name, column in synthea_features.items():\n","  dtype = column.dtype\n","  if dtype == object:\n","    dtype = tf.string\n","  else:\n","    dtype = tf.float32\n","\n","  inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)\n","\n","inputs"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'BASE_COST': <tf.Tensor 'BASE_COST:0' shape=(None, 1) dtype=float32>,\n"," 'DESCRIPTION': <tf.Tensor 'DESCRIPTION:0' shape=(None, 1) dtype=string>,\n"," 'DISPENSES': <tf.Tensor 'DISPENSES:0' shape=(None, 1) dtype=float32>,\n"," 'ENCOUNTER': <tf.Tensor 'ENCOUNTER:0' shape=(None, 1) dtype=string>,\n"," 'PATIENT': <tf.Tensor 'PATIENT:0' shape=(None, 1) dtype=string>,\n"," 'PAYER': <tf.Tensor 'PAYER:0' shape=(None, 1) dtype=string>,\n"," 'PAYER_COVERAGE': <tf.Tensor 'PAYER_COVERAGE:0' shape=(None, 1) dtype=float32>,\n"," 'REASONCODE': <tf.Tensor 'REASONCODE:0' shape=(None, 1) dtype=float32>,\n"," 'REASONDESCRIPTION': <tf.Tensor 'REASONDESCRIPTION:0' shape=(None, 1) dtype=string>,\n"," 'START': <tf.Tensor 'START:0' shape=(None, 1) dtype=string>,\n"," 'STOP': <tf.Tensor 'STOP:0' shape=(None, 1) dtype=string>,\n"," 'TOTALCOST': <tf.Tensor 'TOTALCOST:0' shape=(None, 1) dtype=float32>}"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"tBD7ZpOOq5XY"},"source":["The first step in your preprocessing logic is to concatenate the numeric inputs together, and run them through a normalization layer:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lH-F9cedq6Kz","executionInfo":{"status":"ok","timestamp":1607000792364,"user_tz":0,"elapsed":502,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"39c4dbcf-6ab9-4bc2-a41a-44b893a72506"},"source":["numeric_inputs = {name:input for name,input in inputs.items()\n","                  if input.dtype==tf.float32}\n","\n","x = layers.Concatenate()(list(numeric_inputs.values()))\n","norm = preprocessing.Normalization()\n","norm.adapt(np.array(synthea[numeric_inputs.keys()]))\n","all_numeric_inputs = norm(x)\n","\n","all_numeric_inputs"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor 'normalization/truediv:0' shape=(None, 5) dtype=float32>"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"D_vrSUIvq7nf"},"source":["Collect all the symbolic preprocessing results, to concatenate them later."]},{"cell_type":"code","metadata":{"id":"zLxHjasDq-Fm","executionInfo":{"status":"ok","timestamp":1607000794751,"user_tz":0,"elapsed":636,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["preprocessed_inputs = [all_numeric_inputs]"],"execution_count":15,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"s6P_rIBQq_0H"},"source":["For the string inputs use the `preprocessing.StringLookup` function to map from strings to integer indices in a vocabulary. Next, use `preprocessing.CategoryEncoding` to convert the indexes into `float32` data appropriate for the model. \n","\n","The default settings for the `preprocessing.CategoryEncoding` layer create a one-hot vector for each input. A `layers.Embedding` would also work. See the [preprocessing layers guide](https://www.tensorflow.org/guide/keras/preprocessing_layers#quick_recipes) and [tutorial](../structured_data/preprocessing_layers.ipynb) for more on this topic."]},{"cell_type":"code","metadata":{"id":"nWiJQU6JrB7a","executionInfo":{"status":"ok","timestamp":1607000797943,"user_tz":0,"elapsed":1821,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["for name, input in inputs.items():\n","  if input.dtype == tf.float32:\n","    continue\n","  \n","  lookup = preprocessing.StringLookup(vocabulary=np.unique(synthea_features[name]))\n","  one_hot = preprocessing.CategoryEncoding(max_tokens=lookup.vocab_size())\n","\n","  x = lookup(input)\n","  x = one_hot(x)\n","  preprocessed_inputs.append(x)"],"execution_count":16,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uzo7sU7GrDvr"},"source":["With the collection of `inputs` and `processed_inputs`, you can concatenate all the preprocessed inputs together, and build a model that handles the preprocessing:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"B-hukPtnrFZQ","executionInfo":{"status":"ok","timestamp":1607000801031,"user_tz":0,"elapsed":1481,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"86c3f3b9-8394-42c0-bdc6-df4f2a40f339"},"source":["preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)\n","\n","synthea_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)\n","\n","tf.keras.utils.plot_model(model = synthea_preprocessing , rankdir=\"LR\", dpi=72, show_shapes=True)"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"image/png":"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object>"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"M_K1GyJ7rHLb"},"source":["This `model` just contains the input preprocessing. You can run it to see what it does to your data. Keras models don't automatically convert Pandas `DataFrames` because it's not clear if it should be converted to one tensor or to a dictionary of tensors. So convert it to a dictionary of tensors:"]},{"cell_type":"code","metadata":{"id":"ZIJnNwbwrJOA","executionInfo":{"status":"ok","timestamp":1607000803853,"user_tz":0,"elapsed":456,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["synthea_features_dict = {name: np.array(value) \n","                         for name, value in synthea_features.items()}"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qq362SGcrK6M"},"source":["Slice out the first training example and pass it to this preprocessing model, you see the numeric features and string one-hots all concatenated together:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AxXwWpvjrMvt","executionInfo":{"status":"ok","timestamp":1607000805826,"user_tz":0,"elapsed":520,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"70793694-5acb-4a4b-f1fe-c4546e22e9fb"},"source":["features_dict = {name:values[:1] for name, values in synthea_features_dict.items()}\n","synthea_preprocessing(features_dict)"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(1, 309012), dtype=float32, numpy=\n","array([[-0.191, -0.136, -0.726, ...,  0.   ,  0.   ,  1.   ]],\n","      dtype=float32)>"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"ZKa93zScrOIg"},"source":["Now build the model on top of this:"]},{"cell_type":"code","metadata":{"id":"bAeEV5HNrPxa","executionInfo":{"status":"ok","timestamp":1607000807722,"user_tz":0,"elapsed":461,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}}},"source":["def synthea_model(preprocessing_head, inputs):\n","  body = tf.keras.Sequential([\n","    layers.Dense(64),\n","    layers.Dense(1)\n","  ])\n","\n","  preprocessed_inputs = preprocessing_head(inputs)\n","  result = body(preprocessed_inputs)\n","  model = tf.keras.Model(inputs, result)\n","\n","  model.compile(loss=tf.losses.BinaryCrossentropy(from_logits=True),\n","                optimizer=tf.optimizers.Adam())\n","  return model\n","\n","synthea_model = synthea_model(synthea_preprocessing, inputs)"],"execution_count":20,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zBpqosLxrRVl"},"source":["When you train the model, pass the dictionary of features as `x`, and the label as `y`."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Rx_0kvBrS8q","executionInfo":{"status":"ok","timestamp":1607001674763,"user_tz":0,"elapsed":865331,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"92a938c1-666f-42bf-bb0c-2d5af78d9782"},"source":["synthea_model.fit(x=synthea_features_dict, y=synthea_labels, epochs=10)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","6095/6095 [==============================] - 89s 15ms/step - loss: -3017927680.0000\n","Epoch 2/10\n","6095/6095 [==============================] - 87s 14ms/step - loss: -17254270976.0000\n","Epoch 3/10\n","6095/6095 [==============================] - 87s 14ms/step - loss: -41967202304.0000\n","Epoch 4/10\n","6095/6095 [==============================] - 87s 14ms/step - loss: -76612575232.0000\n","Epoch 5/10\n","6095/6095 [==============================] - 86s 14ms/step - loss: -121160646656.0000\n","Epoch 6/10\n","6095/6095 [==============================] - 86s 14ms/step - loss: -175352283136.0000\n","Epoch 7/10\n","6095/6095 [==============================] - 85s 14ms/step - loss: -239183183872.0000\n","Epoch 8/10\n","6095/6095 [==============================] - 85s 14ms/step - loss: -312897077248.0000\n","Epoch 9/10\n","6095/6095 [==============================] - 85s 14ms/step - loss: -396135432192.0000\n","Epoch 10/10\n","6095/6095 [==============================] - 85s 14ms/step - loss: -489054306304.0000\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7fa2f01826a0>"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"aq5NZ44-rVke"},"source":["Since the preprocessing is part of the model, you can save the model and reload it somewhere else and get identical results:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wgJ1zX0zrXm3","executionInfo":{"status":"ok","timestamp":1607001751327,"user_tz":0,"elapsed":6851,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"5b08c142-af62-4e8b-d8e7-c9b73954b00f"},"source":["synthea_model.save('test')\n","reloaded = tf.keras.models.load_model('test')"],"execution_count":22,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n","INFO:tensorflow:Assets written to: test/assets\n","WARNING:tensorflow:5 out of the last 5 calls to <function recreate_function.<locals>.restored_function_body at 0x7fa290487158> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:6 out of the last 6 calls to <function recreate_function.<locals>.restored_function_body at 0x7fa3087fbc80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n","WARNING:tensorflow:7 out of the last 7 calls to <function recreate_function.<locals>.restored_function_body at 0x7fa29015e268> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9spV0kkYraws","executionInfo":{"status":"ok","timestamp":1607001756238,"user_tz":0,"elapsed":529,"user":{"displayName":"Pavlos Papadopoulos","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIKrYve_PDQSSr1S5TGncqH1LVxr-44RdcEvoLHQ=s64","userId":"05172076592700520335"}},"outputId":"c31af425-0e82-4207-e359-ae01ae819804"},"source":["features_dict = {name:values[:1] for name, values in synthea_features_dict.items()}\n","\n","before = synthea_model(features_dict)\n","after = reloaded(features_dict)\n","assert (before-after)<1e-3\n","print(before)\n","print(after)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["tf.Tensor([[795121.1]], shape=(1, 1), dtype=float32)\n","tf.Tensor([[795121.1]], shape=(1, 1), dtype=float32)\n"],"name":"stdout"}]}]}